Zebra: Static and Dynamic Genome Cover Thresholds with Overlapping References.
Zebra: Static and Dynamic Genome Cover Thresholds with Overlapping References.
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DOI：
10.1128/msystems.00758-22
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发表时间：
2022-10-26
期刊：
影响因子：
6.4
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中科院分区：
文献类型：
--
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微生物分类学在微生物组研究中仍然是一个具有挑战性的话题，这主要是由于与多个参考基因组重叠的读段的模糊性。随着Web of Life（WoL）参考数据库托管10，575个参考基因组并不断增长，模糊读取的百分比只会增加。由此产生的伪像既产生了共现的错觉，又产生了混淆解释的无关参考命中的长尾。我们引入了基因组覆盖率，即参考基因组与读数重叠的分数，以区分这些伪影。我们展示了如何通过读段计数动态预测基因组覆盖，并在金黄色葡萄球菌单培养中检查我们的模型。我们的模型清晰地分离了两个S。金黄色葡萄球菌和来自参考伪影的真实污染物重叠。我们接下来引入饱和基因组覆盖，参考基因组与样本内容重叠的真实分数。对于低丰度或低流行率的细菌，基因组覆盖可能不饱和。我们通过对大量人类粪便数据集的研究来缓解这种担忧。通过在相似的样本中合成度量，即使对于稀有物种，基因组覆盖也会饱和。我们注意到，它是饱和基因组覆盖率的阈值，而不是基因组覆盖率本身，这表明虚假的参考命中或远亲。我们提出了斑马，一种方法来计算和阈值的基因组覆盖度量在类似的样本，递归估计基因组覆盖和确认饱和度，并提供指导选择覆盖阈值在真实的世界的情况下。独立的基因组覆盖和整合到Woltka中可在https：//github.com/biocore/zebra_filter，https://github.com/qiyunzhu/woltka上获得。重要信息分类学分配，将序列分配到特定的分类单位，是微生物组分析的关键处理步骤。分类分配中的问题影响对每个样本中存在哪些微生物的解释，并且可能与特定的环境或临床条件相关。对一个特定分类单元的重要性强烈依赖于分配计数的独立性。错误地包含数千个相关的分类群使得解释模糊不清，导致无法复制的约束不足的结果。有时候对炭疽或淋巴腺鼠疫等难以置信的人工制品的重视尤其成问题。我们表明，斑马过滤器检索的样品内容，使更多的再现性和生物合理的解释宏基因组数据的最近的亲属。
Assigning taxonomy remains a challenging topic in microbiome studies, due largely to ambiguity of reads which overlap multiple reference genomes. With the Web of Life (WoL) reference database hosting 10,575 reference genomes and growing, the percentage of ambiguous reads will only increase. The resulting artifacts create both the illusion of co-occurrence and a long tail end of extraneous reference hits that confound interpretation. We introduce genome cover, the fraction of reference genome overlapped by reads, to distinguish these artifacts. We show how to dynamically predict genome cover by read count and examine our model in Staphylococcus aureus monoculture. Our modeling cleanly separates both S. aureus and true contaminants from the false artifacts of reference overlap. We next introduce saturated genome cover, the true fraction of a reference genome overlapped by sample contents. Genome cover may not saturate for low abundance or low prevalence bacteria. We assuage this worry with examination of a large human fecal data set. By compositing the metric across like samples, genome cover saturates even for rare species. We note that it is a threshold on saturated genome cover, not genome cover itself, which indicates a spurious reference hit or distant relative. We present Zebra, a method to compute and threshold the genome cover metric across like samples, a recurrence to estimate genome cover and confirm saturation, and provide guidance for choosing cover thresholds in real world scenarios. Standalone genome cover and integration into Woltka are available: https://github.com/biocore/zebra_filter, https://github.com/qiyunzhu/woltka. IMPORTANCE Taxonomic assignment, assigning sequences to specific taxonomic units, is a crucial processing step in microbiome analyses. Issues in taxonomic assignment affect interpretation of what microbes are present in each sample and may be associated with specific environmental or clinical conditions. Assigning importance to a particular taxon relies strongly on independence of assigned counts. The false inclusion of thousands of correlated taxa makes interpretation ambiguous, leading to underconstrained results which cannot be reproduced. The importance sometimes attached to implausible artifacts such as anthrax or bubonic plague is especially problematic. We show that the Zebra filter retrieves only the nearest relatives of sample contents enabling more reproducible and biologically plausible interpretation of metagenomic data.
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影响因子：
4.3
作者：
Mallick H;Rahnavard A;McIver LJ;Ma S;Zhang Y;Nguyen LH;Tickle TL;Weingart G;Ren B;Schwager EH;Chatterjee S;Thompson KN;Wilkinson JE;Subramanian A;Lu Y;Waldron L;Paulson JN;Franzosa EA;Bravo HC;Huttenhower C
通讯作者：
Huttenhower C
影响因子：
16.6
作者：
Zhu, Qiyun;Mai, Uyen;Knight, Rob
通讯作者：
Knight, Rob
影响因子：
12.3
作者：
Breitwieser FP;Baker DN;Salzberg SL
通讯作者：
Salzberg SL
影响因子：
48
作者：
Gonzalez A;Navas-Molina JA;Kosciolek T;McDonald D;Vázquez-Baeza Y;Ackermann G;DeReus J;Janssen S;Swafford AD;Orchanian SB;Sanders JG;Shorenstein J;Holste H;Petrus S;Robbins-Pianka A;Brislawn CJ;Wang M;Rideout JR;Bolyen E;Dillon M;Caporaso JG;Dorrestein PC;Knight R
通讯作者：
Knight R
影响因子：
6.4
作者：
Hsu T;Joice R;Vallarino J;Abu-Ali G;Hartmann EM;Shafquat A;DuLong C;Baranowski C;Gevers D;Green JL;Morgan XC;Spengler JD;Huttenhower C
通讯作者：
Huttenhower C